We are exploring ways of translating imaging science to patient diagnosis and in particular to the detection of pre-clinical atrophy signatures of various neurodegenerative disorders. We use image classification methods largely based on machine learning and graphical approaches and attempt to validate results with pathologically verified material. We have succeeded in showing proof of principle using support vector machines and now want to explore the methods so that we can optimise sensitivity and maximise the chances of detecting early change when functional compensation successfully postpones cognitive change. Our collaboration with mathematicians in the group is critical to the development of the analytical techniques. We have international collaborations with John Ashburner at the FIL in London, Stefan Kloppel in Freiburg-im-Giesau and physics collaborations in both the UK and Germany. We work on the ADNI data set and also collect scans from colleagues world wide who have pathologically verified data to validate the methods developed. The possibility of grading disease by extent of atrophy and measuring cognitive reserve by correlation with behavioural tests are major aims. The notion that early treatment with degeneration decelerating agents may postpone disease manifestations effectively is our guiding principle. Finally we want to associate anatomical change profiles with genetic biomarkers of the same neurodegenerative disorders

Research topics

• Effects of pooling scans from different sources and at different magnetic strengths

• Correlations between signature patterns of anatomical change and cognitive or behavioural profiles

• Generalisation of the techniques by a combination of mass data farming (e.g., all structural scans from a defined geographical area in the over 50s) and data trawling looking for patterns of atrophy using image classification to do epidemiology, create atrophy degree dependant cohorts for therapeutic trials etc…